Introduction
The rapid advancement of digital technologies is fundamentally reshaping learning environments, with generative artificial intelligence (AI)emerging as one of the most transformative forces in education. Unlike traditional AI, generative AI possesses the unique capability to create new content, design dynamic learning pathways, and provide adaptive feedback, thereby challenging the conventional paradigms of curriculum development and1 the traditional roles of teachers (Kaban, 2023; Kaplan-Rakowski et al., 2023). Globally, leading educational technology institutions are leveraging generative AI to move away from the 'one-size-fits-all' curriculum model toward personalized, data-driven learning trajectories (Chen et al., 2023; UNESCO, 2023).
Despite these immense opportunities, the integration of generative AI into educational systems presents profound challenges. These include algorithmic bias, data privacy concerns, the widening digital divide, and the urgent need for comprehensive teacher retraining (UNESCO, 2023; Kamalov et al., 2023). In the context of Iran, the post-COVID-19 era has highlighted a critical need for targeted professional development to help teachers effectively integrate new technologies. While existing research has explored AI in education, a significant gap remains in systematically identifying the specific components through which generative AI will influence the future of curricula and educational systems, particularly from a future-studies perspective. Therefore, this study aims to address the following research questions: (1)What is the role of generative AI in designing and developing future curricula? (2)What are the impacts of generative AI on the role of teachers in future educational systems? and (3)How can generative AI personalize learning for students in future educational systems?

Methodology
This study employed a systematic review method following the PRISMA 2020 guidelines to identify and synthesize key components influencing the future of educational systems and curricula under the influence of generative AI. A systematic search was conducted across reputable international databases (Google Scholar, Scopus, IEEE Xplore)and national databases (Noormags, Magiran)using relevant keywords, including "generative AI," "curriculum," "educational futurology," and "ChatGPT," covering the period from 2015 to mid-2025.
The initial search yielded 530 articles. After removing duplicates, 510 articles underwent a two-stage screening process. In the first stage, based on title and abstract review, 418 articles were excluded for not meeting the inclusion criteria (e.g., not focusing on generative AI and future-oriented curriculum planning). In the second stage, the full texts of the remaining 92 articles were assessed, leading to the exclusion of 44 more articles due to lack of direct relevance, inaccessible full text, or insufficient methodological quality. Ultimately, 48 articles were deemed eligible and included in the final analysis. Data extraction and analysis were conducted using thematic coding in MAXQDA software. The quality of the selected studies was independently assessed by two researchers using the Critical Appraisal Skills Programme (CASP)checklist to ensure methodological rigor. The final synthesis organized the findings into 4 main themes and 19 sub-components, providing a comprehensive framework for understanding the transformative role of generative AI in future education.

Results
The analysis of the 48 selected studies yielded comprehensive answers to the three research questions. For the first question on curriculum development, generative AI’s role is defined by four main components: personalization of curriculum (35%), creation of impactful learning experiences (25%), educational justice and equality (20%), and continuous quality improvement (20%). The findings indicate that generative AI acts as an intelligent design partner, moving beyond static curricula to create dynamic, responsive, and highly customized learning pathways based on individual learner data (Zhang & Aslan, 2024; Bozkurt, 2024).
Regarding the second question on the role of teachers, the results reveal a fundamental shift. The traditional role of teacher as sole knowledge transmitter is transforming into five key areas: transformation in instructional design (25%), change in educational assessment (20%), specialization of data-driven educational support (20%), continuous professional development (18%), and strengthening of the nurturing role (17%). This indicates that rather than replacing teachers, generative AI will elevate their role to that of facilitators, learning experience designers, and cultivators of irreplaceable human skills like critical thinking and creativity (Ertmer & Ottenbreit-Leftwich, 2010).
For the third question on personalized learning, the synthesis identified five primary mechanisms: customized educational content production (30%), design of flexible learning pathways (25%), immediate and constructive feedback (20%), difficulty level adaptation (15%), and 24/7 comprehensive support (10%). The results show that generative AI creates a unique learning journey for each student by analyzing their cognitive and emotional profiles, adjusting the pace and content in real-time, and ensuring that instruction remains within each learner’s zone of proximal development, thereby enhancing both academic outcomes and intrinsic motivation.

Conclusion
This systematic review concludes that generative AI is not merely an incremental technological tool but a fundamental catalyst for a new educational paradigm. The findings demonstrate that the future of curricula depends on an intelligent synergy between teachers' professional judgment and the generative capabilities of AI. This synergy promises a transition from standardized, rigid systems to dynamic, personalized, and equitable learning ecosystems. Generative AI empowers the creation of adaptive curricula, transforms teachers into high-level facilitators and mentors, and personalizes learning in unprecedented ways, fostering deeper engagement and lifelong learning.
However, realizing this transformative vision requires addressing significant challenges, including algorithmic bias, ethical concerns, the digital divide, and the necessity for substantial teacher retraining. The study concludes that successful implementation is contingent upon three critical prerequisites: (1)investment in robust data infrastructure and technological resources, (2)development of strong ethical and governance frameworks at the national and institutional levels, and (3)fundamental redesign of teacher professional development and pre-service training programs to cultivate digital and analytical competencies. Ultimately, the future of education lies in a human-AI partnership where technology serves the goal of cultivating creative, critical, and ethical citizens, making collaborative investment from policymakers, educational leaders, and researchers indispensable.